253 citations · 265 across the 17 of their papers we have counts for
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cs.CV2022
In Search of Projectively Equivariant Networks
Georg Bökman, Axel Flinth, Fredrik Kahl
Equivariance of linear neural network layers is well studied. In this work, we relax the equivariance condition to only be true in a projective sense. We propose a way to construct…
cs.CV2022★ 7 cited
A case for using rotation invariant features in state of the art feature matchers
Georg Bökman, Fredrik Kahl
The aim of this paper is to demonstrate that a state of the art feature matcher (LoFTR) can be made more robust to rotations by simply replacing the backbone CNN with a steerable C…